Empirical Inference
Combinatorial problems with submodular cost functions have recently drawn interest. In a standard combinatorial problem, the sum-of-weights cost is replaced by a submodular set function. The result is a powerful model that is though very hard. In this talk, I will introduce cooperative cuts, minimum cuts with submodular edge weights. I will outline methods to approximately solve this problem, and show an application in computer vision. If time permits, the talk will also sketch regret-minimizing online algorithms for submodular-cost combinatorial problems. This is joint work with Jeff Bilmes (University of Washington).
| Author(s): | Jegelka, S. |
| Links: | |
| Year: | 2011 |
| Month: | March |
| Day: | 0 |
| BibTeX Type: | Talk (talk) |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Event Name: | COSA Workshop: Combinatorial Optimization, Statistics, and Applications |
| Event Place: | München, Germany |
BibTeX
@talk{Jegelka2011,
title = {Cooperative Cuts},
abstract = {Combinatorial problems with submodular cost functions have recently drawn interest. In a standard combinatorial problem, the sum-of-weights cost is replaced by a submodular set function. The result is a powerful model that is though very hard. In this talk, I will introduce cooperative cuts, minimum cuts with submodular edge weights. I will outline methods to approximately solve this problem, and show an application in computer vision. If time permits, the talk will also sketch regret-minimizing online algorithms for submodular-cost combinatorial problems. This is joint work with Jeff Bilmes (University of Washington). },
month = mar,
year = {2011},
author = {Jegelka, S.},
month_numeric = {3}
}